Integrate improved CPU depthwise convolution kernels

* Replace assembly kernels for depthwise convolution with more optimized
  ones.
* Add int8 assembly kernels.
* Fix implicit padding on optimized kernels

Resolves: COMPMID-3867, COMPMID-4361

Change-Id: I0b0867e05f61be4f368f62190d55e14d0ab3ebf2
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5622
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

1 files changed, 347 insertions, 0 deletions

diff --git a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst.hpp b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst.hpp
new file mode 100644
index 0000000000..fe635d6fad
--- /dev/null
+++ b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_depthfirst.hpp
@@ -0,0 +1,347 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include "src/core/NEON/kernels/arm_gemm/utils.hpp"
+
+#ifdef CYCLE_PROFILING
+#include "profiler.hpp"
+#endif
+
+namespace arm_conv {
+namespace depthwise {
+
+template <class strategy>
+class DepthwiseDepthfirst : public DepthwiseCommon<typename strategy::input_type,
+                                                   typename strategy::weight_type,
+                                                   typename strategy::return_type>
+{
+  using TInput = typename strategy::input_type;
+  using TWeight = typename strategy::weight_type;
+  using TOutput = typename strategy::return_type;
+  using TAccum = typename strategy::bias_type;
+
+  size_t sizeof_input_buffer(unsigned int n_input_channels) const
+  {
+    return sizeof(TInput) * n_input_channels;
+  }
+
+  size_t sizeof_output_buffer(unsigned int n_output_channels) const
+  {
+    return sizeof(TOutput) * n_output_channels;
+  }
+
+  public:
+
+  DepthwiseDepthfirst(const DepthwiseArgs &args) : DepthwiseCommon<TInput, TWeight, TOutput>(args)
+  {
+  }
+
+  DepthwiseDepthfirst(DepthwiseDepthfirst &) = delete;
+  DepthwiseDepthfirst &operator=(DepthwiseDepthfirst &) = delete;
+
+  size_t get_storage_size(void) const override
+  {
+    // TODO What if we insert extra padding? Biases are a different size to the inputs, ...
+    const unsigned int vl = arm_gemm::utils::get_vector_length<TInput>(strategy::vl_type);
+    const auto rounded_channels = arm_gemm::roundup(this->m_args.input_channels, vl);
+    return (1 + this->m_args.kernel_rows * this->m_args.kernel_cols) * rounded_channels * sizeof(TWeight);
+  }
+
+  void pack_parameters(void *_buffer, const void *_biases, const void *_weights, size_t ld_weight_col, size_t ld_weight_row) override
+  {
+    // TODO What if the kernel needs a different packing function?
+
+    // Cast the pointers
+    uint8_t *buffer = static_cast<uint8_t *>(_buffer);
+    const TAccum *biases = static_cast<const TAccum *>(_biases);
+    const TWeight *const weights = static_cast<const TWeight *>(_weights);
+
+    const unsigned int vl = arm_gemm::utils::get_vector_length<TAccum>(strategy::vl_type);
+    ld_weight_col = (ld_weight_col == 0) ? this->m_args.input_channels : ld_weight_col;
+    ld_weight_row = (ld_weight_row == 0) ? this->m_args.kernel_cols * ld_weight_col : ld_weight_row;
+
+    for (unsigned int n = 0; n < this->m_args.input_channels; n += vl)
+    {
+      const unsigned int todo = std::min(vl, this->m_args.input_channels - n);
+
+      // Copy across the correct amount of bias (or 0)
+      for (unsigned int i = 0; i < todo; i++)
+      {
+        reinterpret_cast<TAccum *>(buffer)[i] = (biases == nullptr) ? 0 : biases[n + i];
+      }
+      buffer += vl * sizeof(TAccum);
+
+      // Copy each of the weights in turn
+      auto weights_row = weights + n;
+      for (unsigned int i = 0; i < this->m_args.kernel_rows; i++)
+      {
+        auto weights_col = weights_row;
+
+        for (unsigned int j = 0; j < this->m_args.kernel_cols; j++)
+        {
+          for (unsigned int m = 0; m < todo; m++)
+          {
+            reinterpret_cast<TWeight *>(buffer)[m] = weights_col[m];
+          }
+          buffer += vl * sizeof(TWeight);
+
+          weights_col += ld_weight_col;
+        }
+
+        weights_row += ld_weight_row;
+      }
+    }
+  }
+
+  size_t get_working_size(const unsigned int n_threads, const unsigned int n_channels) const override
+  {
+    const unsigned int n_output_channels = n_channels * this->m_args.channel_multiplier;
+    return n_threads * (sizeof_output_buffer(n_output_channels) + sizeof_input_buffer(n_channels));
+  }
+
+  using DepthwiseCommon<typename strategy::input_type, typename strategy::weight_type, typename strategy::return_type>::execute;
+  void execute(
+    const unsigned int batches,
+    const unsigned int input_height,
+    const unsigned int input_width,
+    const unsigned int input_channels,
+    const PaddingValues &padding,
+    const void *const _input,
+    const size_t ld_input_col,
+    const size_t ld_input_row,
+    const size_t ld_input_batch,
+    const void *const parameters,
+    const unsigned int output_height,
+    const unsigned int output_width,
+    void *const _output,
+    const size_t ld_output_col,
+    const size_t ld_output_row,
+    const size_t ld_output_batch,
+    void *const _working_space,
+    const unsigned int thread_id,
+    const unsigned int n_threads
+  ) const override
+  {
+    strategy strat(this->m_args.cpu_info);
+#ifdef CYCLE_PROFILING
+    arm_gemm::profiler prof;
+#endif
+
+    // Compute activation values
+    TAccum activation_min, activation_max;
+    if (std::numeric_limits<TAccum>::is_integer)
+    {
+      activation_min = std::numeric_limits<TAccum>::min();
+      activation_max = std::numeric_limits<TAccum>::max();
+    }
+    else
+    {
+      activation_min = static_cast<TAccum>(-std::numeric_limits<float>::infinity());
+      activation_max = static_cast<TAccum>(std::numeric_limits<float>::infinity());
+    }
+
+    switch (this->m_args.activation.type)
+    {
+      case arm_gemm::Activation::Type::BoundedReLU:
+        activation_max = static_cast<TAccum>(this->m_args.activation.param1);
+        // Fall through
+      case arm_gemm::Activation::Type::ReLU:
+        activation_min = static_cast<TAccum>(0);
+        break;
+      default:
+        break;
+    }
+
+    // Determine what portion of the work to do.
+    const unsigned int n_rows_per_thread = arm_gemm::iceildiv(output_height, n_threads);
+    const int start_out_height = std::min(thread_id * n_rows_per_thread, output_height);
+    const int end_out_height = std::min(start_out_height + n_rows_per_thread, output_height);
+
+    // Cast input and output pointers into the right types
+    const TInput *const inptr = static_cast<const TInput *>(_input);
+    TOutput *const outptr = static_cast<TOutput *>(_output);
+
+    // Create an array for the input pointers
+    const TInput * _inptr_array[strategy::input_rows * strategy::input_cols];
+    const TInput **const inptr_array = _inptr_array;
+
+    // Create an array for the output pointers
+    TOutput * _outptr_array[strategy::output_rows * strategy::output_cols];
+    TOutput **const outptr_array = _outptr_array;
+
+    // Allocate portions of the working space
+    uint8_t *const working_space = static_cast<uint8_t *>(_working_space) + get_working_size(thread_id, input_channels);
+    TOutput *const output_buffer = reinterpret_cast<TOutput *>(working_space);
+    TInput *const input_buffer = reinterpret_cast<TInput *>(working_space + sizeof_output_buffer(input_channels * this->m_args.channel_multiplier));
+
+    // Initialise the input buffer
+    for (unsigned int c = 0; c < input_channels; c++)
+    {
+      input_buffer[c] = static_cast<TInput>(0);
+    }
+
+    // For each output tile, construct the requisite set of pointers and call
+    // into the kernel.
+    for (unsigned int batch = 0; batch < batches; batch++)
+    {
+      // Get batch pointers
+      const auto inptr_batch = inptr + batch * ld_input_batch;
+      const auto outptr_batch = outptr + batch * ld_output_batch;
+
+      for (int start_out_i = start_out_height;
+           start_out_i < end_out_height;
+           start_out_i += static_cast<int>(strategy::output_rows))
+      {
+        const int end_out_i = start_out_i + strategy::output_rows;
+        const int start_in_i = start_out_i * strategy::stride_rows - padding.top;
+        const int end_in_i = start_in_i + strategy::input_rows;
+
+        // Compute top/bottom padding
+        const auto pad_top = static_cast<unsigned int>(-std::min(start_in_i, 0));
+        const auto pad_bottom = static_cast<unsigned int>(-std::min(static_cast<int>(input_height) - end_in_i, 0));
+        const unsigned int valid_output_rows = std::min(
+          end_out_i - start_out_i,
+          static_cast<int>(output_height) - start_out_i
+        );
+
+        // Fill the input pointer array with padding values
+        for (auto index = 0u; index < strategy::input_rows * strategy::input_cols; index++)
+        {
+          inptr_array[index] = input_buffer;
+        }
+
+        for (int start_out_j = 0; start_out_j < static_cast<int>(output_width);)
+        {
+          const int start_in_j = start_out_j * strategy::stride_cols - this->m_args.padding.left;
+          const int pad_left = -std::min(0, start_in_j);
+
+          // Compute how many output tiles we can compute with the direct kernel.
+          int n_direct_tiles = 0;
+          if (!pad_top && !pad_bottom && !pad_left)
+          {
+            // Determine the maximum number of tiles we could handle.
+            n_direct_tiles = (output_width - start_out_j) / strategy::output_cols;
+
+            // Continue to reduce this number as required to avoid reading
+            // padding on the right edge.
+            int end_in_j = start_in_j + n_direct_tiles * strategy::input_cols;
+            int pad_right = std::max(0, end_in_j - static_cast<int>(input_width));
+
+            while (pad_right && n_direct_tiles)
+            {
+              n_direct_tiles--;
+              end_in_j -= strategy::input_cols;
+              pad_right = std::max(0, end_in_j - static_cast<int>(input_width));
+            }
+          }
+
+          // Use the unpadded kernel if we can, otherwise use the padded one.
+          if (n_direct_tiles)
+          {
+            auto inptr = inptr_batch + start_in_i*ld_input_row + start_in_j*ld_input_col;
+            auto outptr = outptr_batch + start_out_i*ld_output_row + start_out_j*ld_output_col;
+            start_out_j += n_direct_tiles*strategy::output_cols;
+
+#ifdef CYCLE_PROFILING
+            auto p = prof.ScopedProfiler(PROFILE_KERNEL, 0);
+#endif
+            strat.direct_kernel(1, n_direct_tiles,
+                                inptr, ld_input_row, ld_input_col,
+                                outptr, ld_output_row, ld_output_col,
+                                parameters, this->m_args.input_channels,
+                                activation_min, activation_max);
+            continue;
+          }
+
+          const int end_out_j = start_out_j + strategy::output_cols;
+          const int end_in_j = start_in_j + strategy::input_cols;
+
+          const auto pad_right = static_cast<unsigned int>(-std::min(static_cast<int>(input_width) - end_in_j, 0));
+          const unsigned int valid_output_cols = std::min(
+            end_out_j - start_out_j,
+            static_cast<int>(output_width) - start_out_j
+          );
+
+          // Construct the input pointer array - fill the array with pointers to
+          // the input buffer and then fill in the required values.
+          for (auto i = pad_top; i < strategy::input_rows - pad_bottom; i++)
+          {
+            // Can skip over the left padding because we will have either the
+            // same or less than the previous tile.
+            unsigned int j = pad_left;
+            const TInput *colptr = inptr_batch + (start_in_i + i) * ld_input_row + (start_in_j + j) * ld_input_col;
+            const TInput **ptrs = inptr_array + i * strategy::input_cols + j;
+            for (; j < strategy::input_cols - pad_right; j++)
+            {
+              *(ptrs++) = colptr;
+              colptr += ld_input_col;
+            }
+            for (; j < strategy::input_cols; j++)
+            {
+              *(ptrs++) = input_buffer;
+            }
+          }
+
+          // Construct the output pointer array.
+          TOutput **outptr_pos = outptr_array;
+          for (auto i = 0u; i < valid_output_rows; i++)
+          {
+            unsigned int j = 0u;
+            TOutput *colptr = outptr_batch + (start_out_i + i) * ld_output_row + start_out_j * ld_output_col;
+            for (; j < valid_output_cols; j++)
+            {
+              *(outptr_pos++) = colptr;
+               colptr += ld_output_col;
+            }
+            for (; j < strategy::output_cols; j++)
+            {
+              *(outptr_pos++) = output_buffer;
+            }
+          }
+          for (auto i = valid_output_rows; i < strategy::output_rows; i++)
+          {
+            for (auto j = 0u; j < strategy::output_cols; j++)
+            {
+              *(outptr_pos++) = output_buffer;
+            }
+          }
+
+          start_out_j += strategy::output_cols;
+
+#ifdef CYCLE_PROFILING
+          // TODO Work number
+          auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)(0));
+#endif
+          strat.indirect_kernel(inptr_array, outptr_array, parameters,
+                                this->m_args.input_channels, activation_min, activation_max);
+        }
+      }
+    }
+  }
+};
+
+}  // namespace depthwise
+}  // namespace arm_conv